基于模型的深度对抗学习的高性能快速磁共振参数映射。
High-performance rapid MR parameter mapping using model-based deep adversarial learning.
机构信息
Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
出版信息
Magn Reson Imaging. 2020 Dec;74:152-160. doi: 10.1016/j.mri.2020.09.021. Epub 2020 Sep 25.
PURPOSE
To develop and evaluate a deep adversarial learning-based image reconstruction approach for rapid and efficient MR parameter mapping.
METHODS
The proposed method provides an image reconstruction framework by combining the end-to-end convolutional neural network (CNN) mapping, adversarial learning, and MR physical models. The CNN performs direct image-to-parameter mapping by transforming a series of undersampled images directly into MR parameter maps. Adversarial learning is used to improve image sharpness and enable better texture restoration during the image-to-parameter conversion. An additional pathway concerning the MR signal model is added between the estimated parameter maps and undersampled k-space data to ensure the data consistency during network training. The proposed framework was evaluated on T mapping of the brain and the knee at an acceleration rate R = 8 and was compared with other state-of-the-art reconstruction methods. Global and regional quantitative assessments were performed to demonstrate the reconstruction performance of the proposed method.
RESULTS
The proposed adversarial learning approach achieved accurate T mapping up to R = 8 in brain and knee joint image datasets. Compared to conventional reconstruction approaches that exploit image sparsity and low-rankness, the proposed method yielded lower errors and higher similarity to the reference and better image sharpness in the T estimation. The quantitative metrics were normalized root mean square error of 3.6% for brain and 7.3% for knee, structural similarity index of 85.1% for brain and 83.2% for knee, and tenengrad measures of 9.2% for brain and 10.1% for the knee. The adversarial approach also achieved better performance for maintaining greater image texture and sharpness in comparison to the CNN approach without adversarial learning.
CONCLUSION
The proposed framework by incorporating the efficient end-to-end CNN mapping, adversarial learning, and physical model enforced data consistency is a promising approach for rapid and efficient reconstruction of quantitative MR parameters.
目的
开发并评估一种基于深度对抗学习的快速高效磁共振参数映射图像重建方法。
方法
该方法提出了一种图像重建框架，通过结合端到端卷积神经网络（CNN）映射、对抗学习和磁共振物理模型来实现。CNN 通过将一系列欠采样图像直接转换为磁共振参数图来执行直接的图像到参数映射。对抗学习用于提高图像锐度，并在图像到参数转换过程中实现更好的纹理恢复。在估计的参数图和欠采样 k 空间数据之间添加了一条与磁共振信号模型相关的附加路径，以确保网络训练过程中的数据一致性。该框架在大脑和膝关节的 T 映射加速率 R=8 上进行了评估，并与其他最先进的重建方法进行了比较。进行了全局和局部定量评估，以证明所提出方法的重建性能。
结果
所提出的对抗学习方法在大脑和膝关节图像数据集上实现了高达 R=8 的准确 T 映射。与利用图像稀疏性和低秩性的传统重建方法相比，该方法产生的误差更低，与参考值的相似度更高，T 估计的图像锐度更好。定量指标为大脑的归一化均方根误差为 3.6%，膝关节为 7.3%；大脑的结构相似性指数为 85.1%，膝关节为 83.2%；大脑的张量梯度测量值为 9.2%，膝关节为 10.1%。与没有对抗学习的 CNN 方法相比，对抗方法在保持更大的图像纹理和锐度方面也表现出更好的性能。
结论
该框架通过结合高效的端到端 CNN 映射、对抗学习和物理模型强制数据一致性，是一种快速高效重建定量磁共振参数的有前途的方法。
Zotero 插件